Aggregate queries are becoming increasingly popular, especially in time series applications (e.g., applications that stream stock prices, sports scores, and the like). Queries from such applications often involve looking back and/or forward in time for a most recent record satisfying some search condition for a set of values (e.g., stock symbols). Many techniques are available for optimizing aggregate queries. These optimization techniques, however, typically require retrieving all qualifying rows (e.g., all rows satisfying one or more search conditions specified in a query) from base relations (e.g., tables involved in the one or more search conditions specified in the query) to compute a result set. For certain types of applications, such as time series applications, the delay resulting from the number of input/outputs (I/Os) needed to compute the result set is unacceptable.